{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\evo\\anaconda\\envs\\tf1.14\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "D:\\evo\\anaconda\\envs\\tf1.14\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "D:\\evo\\anaconda\\envs\\tf1.14\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "D:\\evo\\anaconda\\envs\\tf1.14\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "D:\\evo\\anaconda\\envs\\tf1.14\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "D:\\evo\\anaconda\\envs\\tf1.14\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "D:\\evo\\anaconda\\envs\\tf1.14\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "D:\\evo\\anaconda\\envs\\tf1.14\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "D:\\evo\\anaconda\\envs\\tf1.14\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "D:\\evo\\anaconda\\envs\\tf1.14\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "D:\\evo\\anaconda\\envs\\tf1.14\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "D:\\evo\\anaconda\\envs\\tf1.14\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "# 224\n",
    "test_feature_index = pd.read_csv('./new_user/Test_new_feature_index.csv', index_col=[0])\n",
    "test_feature_value = pd.read_csv('./new_user/Test_new_feature_value.csv', index_col=[0])\n",
    "label = pd.read_csv('./new_user/new_user.csv', index_col=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\S\\AppData\\Local\\Temp/ipykernel_9612/3253270250.py:1: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\S\\AppData\\Local\\Temp/ipykernel_9612/3253270250.py:2: The name tf.global_variables_initializer is deprecated. Please use tf.compat.v1.global_variables_initializer instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\S\\AppData\\Local\\Temp/ipykernel_9612/3253270250.py:5: The name tf.train.import_meta_graph is deprecated. Please use tf.compat.v1.train.import_meta_graph instead.\n",
      "\n",
      "WARNING:tensorflow:From D:\\evo\\anaconda\\envs\\tf1.14\\lib\\site-packages\\tensorflow\\python\\training\\saver.py:1276: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use standard file APIs to check for files with this prefix.\n",
      "INFO:tensorflow:Restoring parameters from ./model_/my_test_model\n",
      "WARNING:tensorflow:From C:\\Users\\S\\AppData\\Local\\Temp/ipykernel_9612/3253270250.py:9: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sess = tf.Session()\n",
    "init = tf.global_variables_initializer()\n",
    "sess.run(init)\n",
    "\n",
    "saver = tf.train.import_meta_graph('./model_/my_test_model.meta')\n",
    "\n",
    "saver.restore(sess, tf.train.latest_checkpoint('./model_/'))\n",
    "\n",
    "graph = tf.get_default_graph()\n",
    "\n",
    "feat_index = graph.get_tensor_by_name(\"feat_index:0\") #特征索引\n",
    "feat_value = graph.get_tensor_by_name(\"feat_value:0\") #特征值\n",
    "out = graph.get_tensor_by_name(\"out:0\") #输出\n",
    "\n",
    "\n",
    "test_feature_index = pd.concat([test_feature_index, test_feature_index,test_feature_index])\n",
    "test_feature_value = pd.concat([test_feature_value, test_feature_value,test_feature_value])\n",
    "label = pd.concat([label, label,label])\n",
    "feed_dict={feat_index:test_feature_index[0:144],feat_value:test_feature_value[0:144]}\n",
    "\n",
    "pre = sess.run(out,feed_dict)\n",
    "\n",
    "pre_res = []\n",
    "for _,i in enumerate(pre):\n",
    "    if i < 0:\n",
    "        pre_res.append(0)\n",
    "    else:\n",
    "        pre_res.append(i) \n",
    "fact_res = label['fdgl'].tolist()[0:144]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "id = [i for i in range(len(pre))]\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.plot(id, fact_res, color='black',linewidth=2,label = '实际功率值')#线1\n",
    "#plt.plot(id, p2,color='green',linewidth=2,linestyle = '--',label = \"基于空间相关性\")#线2\n",
    "plt.plot(id, pre_res,color='red',linewidth=2,linestyle = '--',label = 'DeepFm&LSTM预测')#线2\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.legend(fontsize   = 15)\n",
    "plt.xlabel(\"时间采样点/小时\",fontsize   = 20)\n",
    "plt.ylabel(\"光伏发电功率/kW\",fontsize   = 20)\n",
    "plt.ylim(-1,10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt('./word_data//3_fact.csv',np.array(fact_res[0:48]))\n",
    "\n",
    "np.savetxt('./word_data//3_pre.csv',np.array(pre_res[0:48]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "id = [i for i in range(len(pre_res[0:48]))]\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(id, fact_res[0:48], color='black',linewidth=2,label = '实际功率值')#线1\n",
    "#plt.plot(id, p2,color='green',linewidth=2,linestyle = '--',label = \"基于空间相关性\")#线2\n",
    "plt.plot(id, pre_res[0:48],color='red',linewidth=2,linestyle = '--',label = 'DeepFm&LSTM预测')#线2\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.legend(fontsize   = 15)\n",
    "plt.xlabel(\"时间采样点/小时\",fontsize   = 20)\n",
    "plt.ylabel(\"光伏发电功率/kW\",fontsize   = 20)\n",
    "plt.title(\"对新加入的电站进行预测\",fontsize   = 20)\n",
    "plt.ylim(-1,10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 冷启动问题-新电站预测指标"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# metric_gf\n",
    "def metric_gf(fact_res, pre_res):\n",
    "    from sklearn import metrics\n",
    "    MSE = metrics.mean_squared_error(fact_res,pre_res)\n",
    "    RMSE = metrics.mean_squared_error(fact_res,pre_res)**0.5\n",
    "    MAE = metrics.mean_absolute_error(fact_res,pre_res)\n",
    "    mape_ =[]\n",
    "    c=0\n",
    "    for x,y in zip(fact_res,pre_res):\n",
    "        if x>0.5:\n",
    "            mape_.append(abs((x-y)/x))\n",
    "            c += 1\n",
    "    MAPE = np.array(mape_).sum()/c\n",
    "    return {'RMSE':RMSE,'MAPE':MAPE,'MAE':MAE,'MSE':MSE }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'RMSE': 0.16407417553186285,\n",
       " 'MAPE': 0.13423669338226318,\n",
       " 'MAE': 0.11782842549138967,\n",
       " 'MSE': 0.02692033507646054}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metric_gf(fact_res[0:48], pre_res[0:48])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess.close()"
   ]
  }
 ],
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